Multiple computer systems exist to determine property values, and these current systems are used in modern housing industries for property evaluation and setting prices. Automated valuation models for real estate appraisal typically rely on statistical models such as multiple regression analysis or geographic information systems (GIS). These systems, while widely used, suffer from multiple technical problems that ultimately result in incomplete or inaccurate property value data. The inventor here has recognized several technical problems with such conventional systems, as explained below.
First, current systems determine property value largely based on the average values for a zip code or other predefined neighborhood (such as a county, town, or subdivision). For example, multiple websites exist that allow a user to enter a street address of a property, and the website system estimates a value of the property based on average values for the entered zip code or predefined neighborhood where the property sits. While convenient, these conventional automated valuation models produce inaccurate results when they are used in certain neighborhoods. Many properties are included in these calculations solely because they are in the same general geographic area or zip code, and the resulting values can be very inaccurate when the appraised property does not conform well to the zip code or predefined neighborhood in which it resides. Indeed, many attributes that can differ between properties located in the same zip code or predefined neighborhood, in rural and city areas alike.
Second, current systems rely on traditional relational databases, such as SQL databases and lookup tables. The data architectures underlying such systems is inadequate for storing complex relationships between multiple entities. As a result, traditional relational databases are not technically suited for valuation modeling because of the limited nature of queries that can be executing on such databases. Even where a particular target query can theoretically be constructed from multiple queries on a relational database, multiple query results may need to be combined to acquire the data set necessary for valuation modeling, the database retrieval delays may be large, and additional computational overhead may be needed to combine the query results in a manner relevant to executing the automated valuation models.
As another example, relational databases typically store individual information about the relationships between any two given entities. When new entities and/or relationships are added, database entries grow exponentially to store all new relationships between individual entity pairs. At the scale required in current systems, the storage and computation requirements for maintaining and updating relational databases are unsustainable. Thus, traditional relational database architectures are unsuitable for use in a dynamic system having multiple complex relationships between entities. Such databases are not well suited to representing integrated collections of facts and relationships included in the real estate big data sets, or to extracting, analyzing or manipulating such large data sets in a manner relevant to valuation modeling. Finally, such relational databases are also inefficient for constructing queries for identifying real estate properties similar to other properties, a common type of query in this field.
In view of the technical problems discussed above, there exists a need for technological improvements to current systems.